Sequential Anomaly Detection

Sequential Anomaly Detection is a computational framework within sequence pattern recognition that identifies deviations from expected sequential behaviors by modeling legitimate sequences and detecting instances where observed transitions or ordering of elements significantly diverge from established temporal or structural norms.

118

Publications

5.5K

Citations

391

Authors

179

Institutions

Publications per year

2017–2026

74

Authors

391

  1. QZ
    Qing Zhao

    University of California, Davis, United States

    11 publications · 376 citations

    10

    h-index

  2. KC
    Kobi Cohen

    Ben-Gurion University of the Negev, Israel

    10 publications · 357 citations

    10

    h-index

  3. SS
    Süleyman S. Kozat

    Bilkent University, Türkiye

    4 publications · 116 citations

    4

    h-index

  4. GF
    Göran Falkman

    University of Skövde, Sweden

    3 publications · 307 citations

    3

    h-index

  5. PB
    Paolo Braca

    North Atlantic Treaty Organization, Belgium

    3 publications · 71 citations

    3

    h-index

1–5 of 391

Venues

  1. IEEE Transactions on Information Theory

    5 publications · 252 citations

    5

    h-index

  2. IEEE Transactions on Signal Processing

    5 publications · 185 citations

    5

    h-index

  3. IEEE Access

    2 publications · 36 citations

    2

    h-index

  4. IEEE/ACM Transactions on Networking

    TON · 2 publications · 114 citations

    2

    h-index

  5. 2

    h-index

1–5